from fastapi import FastAPI
from fastmcp import FastMCP
import pandas as pd
from dotenv import load_dotenv
load_dotenv()
mcp = FastMCP(
name="Addition MCP",
)
mcp_app = mcp.streamable_http_app(path="/sse")
app = FastAPI(lifespan=mcp_app.lifespan)
# Method 1: Original decorator approach
@mcp.tool
@app.get("/add", operation_id="add_two_numbers")
async def add(a: int, b: int):
"""Add two numbers and return the sum."""
summ = pd.DataFrame({"a": [a], "b": [b], "sum": [a + b]})
result = int(summ.loc[0, "sum"])
return {"sum": result}
# Method 2: Register function using mcp.tool() as a function call
def multiply(a: int, b: int):
"""Multiply two numbers and return the product."""
product = pd.DataFrame({"a": [a], "b": [b], "product": [a * b]})
result = int(product.loc[0, "product"])
return {"product": result}
# Register the function as a tool
mcp.tool(multiply)
# Method 3: Define function first, then register with mcp.tool()()
def subtract(a: int, b: int):
"""Subtract two numbers and return the difference."""
diff = pd.DataFrame({"a": [a], "b": [b], "difference": [a - b]})
result = int(diff.loc[0, "difference"])
return {"difference": result}
# Register using the decorator syntax as a function
mcp.tool()(subtract)
# Method 4: You can also combine with FastAPI routes
@app.get("/multiply", operation_id="multiply_two_numbers")
async def multiply_endpoint(a: int, b: int):
"""FastAPI endpoint that also works as MCP tool."""
return multiply(a, b)
# Register the same function as an MCP tool
mcp.tool(multiply_endpoint)
app.mount("/mcp-server", mcp_app)
